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Data Assimilation for Ocean Drift Trajectories Using Massive Ensembles and GPUs
(Springer Proceedings in Mathematics & Statistics;volume 323, Conference object, 2020-06-10)In this work, we perform fully nonlinear data assimilation of ocean drift trajectories using multiple GPUs. We use an ensemble of up to 10000 members and the sequential importance resampling algorithm to assimilate ... -
Data center traffic scheduling with hot-cold link detection capabilities
(RACS '18 Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems;, Chapter; Chapter; Peer reviewed, 2018)Software-Defined Networking (SDN) has been one of the most discussed areas in computer networking over the last years. The field has raised an extensive amount of research, and led to a transformation of traditional network ... -
Data fusion without knowledge of the ground truth using Tseltin-like Automata
(Chapter; Peer reviewed; Chapter, 2016)The fusioning of data from unreliable sensors has received much research attention. The main stream of research assesses the reliability of a sensor by comparing its readings to the ground ... -
Data Quality Barriers for Transparency in Public Procurement
(Information; Volume 13, Issue 2, Peer reviewed; Journal article, 2022-02-20)Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major ... -
Data quality issues in solar panels installations: a case study
(SEA4DQ: Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things;SEA4DQ '22: 2nd International Workshop on Software Engineering and AI for Data Quality in Cyber-Physical Systems/Internet of Things, Conference object, 2022-11-09)Solar photovoltaics (PV) is becoming an important source of global electricity generation. Modern PV installations come with a variety of sensors attached to them for monitoring purposes (e.g., maintenance, prediction of ... -
Data quality model for assessing public COVID-19 big datasets
(Peer reviewed; Journal article, 2023)For decision-making support and evidence based on healthcare, high quality data are crucial, particularly if the emphasized knowledge is lacking. For public health practitioners and researchers, the reporting of COVID-19 ... -
Data-driven model of the power-grid frequency dynamics
(IEEE Access;Volume 8, 2020, Peer reviewed; Journal article, 2020-01-20)The energy system is rapidly changing to accommodate the increasing number of renewable generators and the general transition towards a more sustainable future. Simultaneously, business models and market designs evolve, ... -
DataCloud: Enabling the Big Data Pipelines on the Computing Continuum
(Lecture Notes in Business Information Processing;Volume 415, Conference object, 2021-05)With the recent developments of Internet of Things (IoT) and cloud-based technologies, massive amounts of data are generated by heterogeneous sources and stored through dedicated cloud solutions. Often organizations generate ... -
Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration
(Peer reviewed; Journal article, 2023)Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. In that context, distributed computing solutions such as cloud systems are ... -
A dataset for predicting cloud cover over Europe
(Peer reviewed; Journal article, 2024)Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections ... -
A Decentralized Approach for Homogenizing Load Distribution in Cloud Data Center Based on Stable Marriage Matching
(MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystems;, Conference object, 2019)Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators ... -
A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering
(Peer reviewed; Journal article, 2023)An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient ... -
A Deep Learning Approach to Dynamic Passive RTT
(2019 IEEE 38th International Performance Computing and Communications Conference (IPCCC);, Conference object, 2019)The Round-Trip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The end-to-end RTT value influences ... -
A Deep Learning Approach to Dynamic Passive RTT Prediction Model for TCP
(IEEE International Conference Performance, Computing and Communications (IPCCC);, Conference object, 2020-01-16)The Round-Trip Time (RTT) is a property of the path between a sender and a receiver communicating with Transmission Control Protocol (TCP) over an IP network and over the public Internet. The end-to-end RTT value influences ... -
Deep Learning for Classifying Physical Activities from Accelerometer Data
(Sensors;Volume 21 / Issue 16, Peer reviewed; Journal article, 2021-08-18)Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening ... -
Deep learning with cellular automaton-based reservoir computing
(Complex Systems;Volume 26, Issue 4, Journal article; Peer reviewed, 2017)Recurrent neural networks (RNNs) have been a prominent concept wiithin artificial intelligence. They are inspired by biological neural net works (BNNs) and provide an intuitive and abstract representation of how BNNs work. ... -
A deep learning-based tool for automatic brain extraction from functional magnetic resonance images of rodents
(Peer reviewed; Journal article, 2021)Removing skull artifacts from functional magnetic images (fMRI) is a well understood and frequently encountered problem. Because the fMRI field has grown mostly due to human studies, many new tools were developed to handle ... -
Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources
(Sensors;Volume 22 / Issue 7, Peer reviewed; Journal article, 2022-04-06)Many data related problems involve handling multiple data streams of different types at the same time. These problems are both complex and challenging, and researchers often end up using only one modality or combining them ... -
DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine
(Scientific Reports;11, Article number: 21896 (2021), Peer reviewed; Journal article, 2021-11-09)Recent global developments underscore the prominent role big data have in modern medical science. But privacy issues constitute a prevalent problem for collecting and sharing data between researchers. However, synthetic ... -
DeepNAVI: A deep learning based smartphone navigation assistant for people with visual impairments
(Expert Systems With Applications;, Peer reviewed; Journal article, 2022)Navigation assistance is an active research area, where one aim is to foster independent living for people with vision impairments. Despite the fact that many navigation assistants use advanced technologies and methods, ...